铁路道岔故障的智能诊断

An intelligent diagnosis for railway turnout fault

传统的道岔故障检测方式不仅会耗费大量人力、物力、财力,而且检测结果完全依赖于个人工作经验。随着人工智能的飞速发展,研究铁路道岔的智能诊断器是亟待解决的问题。提出一种智能检测系统,该系统从预处理数据、特征提取、构建不均衡数据的智能识别器以及设计更符合要求的评价标准方面进行了具体而深入的研究。最后,通过MATLAB软件对广州钟村站W1902#和W1904#型号的道岔动作电流数据进行仿真实验。实验结果显示,智能检测系统不仅具有非常高的识别性能和泛化能力,而且识别时间仅为0.04 s,满足铁路实时性要求。

The traditional turnout fault detection method not only leads to consume a lot of manpower,material resources and financial resources,but also relies on manual experience.With the rapid development of artificial intelligence,designing an intelligent diagnostic system to diagnose the turnout is a key problem.In this paper,an intelligent detection system is proposed,which contains data preprocessing,feature extraction,switch intelligent classifier and more suitable evaluation criterion design.It is simulated by MATLAB,the experimental results on Guangzhou village station switch current data of model W1902# and model W1904#shows that the current intelligent detection method not only has the ability of self-learning,but also can be detected efficiently in the complex changes of the environment,and the recognition time is only 0.04 s,which meets the real-time requirement of railway.